Artificial intelligence algorithms are designed to learn from data. An AI model can be built based on the learned data, and is flexible enough to perform multiple functions depending on the input provided to the AI model.
However, providing the data to train an AI model is complex. In a less than straightforward example, an AI model configured for recognizing objects is trained using a vast number of object images. For example, a vast number of object images are used for training the AI model. Generation and collection of those images is difficult and very time consuming. Basically, the AI model is trained to recognize every type of object that exists. Imagine trying to collect for each object multiple images of that object that are taken from different perspectives. In that manner, when presented with a new image of an object, the AI model can extract various identifying characteristics (e.g., outline, color, features, size, etc.) to determine if those characteristics match those of a learned object. The number of objects is limitless, and various views of those objects are also limitless. As such, the training of the AI model to recognize objects can be an ongoing process.
It is in this context that embodiments of the disclosure arise.